code: def test_torch_dict_list_stacking():
      unstacked_ex = convert_to_torch(
          unstacked_example(), in_place=True, cast=None, device="cpu"
      )
      stacked_ex = convert_to_torch(
          stacked_example(), in_place=True, cast=None, device="cpu"
      )
  
      stacked = stack_torch_dict_list(unstacked_ex)
      for k, v in stacked_ex.items():
          assert torch.all(v == stacked[k])
  
file_path: maze/maze/test/train/utils/test_train_utils.py
imports: import numpy
  import torch
  from maze.perception.perception_utils import convert_to_torch
  from maze.train.utils.train_utils import unstack_numpy_list_dict, stack_numpy_dict_list, stack_torch_dict_list
label_desc: Use image node_id 6 for calling a global function with example usage: test_torch_dict_list_stacking() without return types